From the right AI bet to a system in production.
We get AI working in your production environment, with governance and unit economics built in from the start rather than bolted on later.
See advisory work →
uniox ~ %
AI advisory · Growth · Capital
Only 4 of 33 enterprise AI proofs ever reach production.
We build for the four.
We help founders ship AI products, enterprises put AI into production, and investors underwrite AI deals. Senior people on every engagement, and work you can defend with numbers, not adjectives.
Isometric diagram of 33 enterprise AI proofs of concept funneling through narrowing layers, with only 4 lit in sapphire at the top, in production.
01Why now
Almost everyone has started. Far fewer have something that pays for itself.
39%
Enterprise profit
of companies see real profit from AI, even though most now use it somewhere.
McKinsey, 202560%
Abandoned
of AI projects without AI-ready data will be dropped through 2026.
Gartner, 202540%+
Canceled
of agentic AI projects are on track to be canceled by 2027, on cost and unclear value.
Gartner, 2025Most AI fails quietly, somewhere between the pilot that impressed everyone and the bill that did not.
02Three lines, one bench
The same senior people, wherever you are in the AI economy.
Go to market like you have sold this before.
We help AI founders find their first repeatable revenue motion and build the pipeline that gets them into a Series A or an enterprise contract with proof. Delivered under RevenueRamp.
Visit RevenueRamp →Deals underwritten by real AI diligence.
We advise on AI transactions with the technical and unit-economics depth that decides whether an AI asset is actually worth what the model says it is.
Explore mandates →03How we work
Four steps. We do not skip ahead to the slides.
- Step 01
Assess
We audit your data, infrastructure, team, and governance gaps before anyone writes code.
- Step 02
Architect
We pick the bet worth making and design the system to fit your problem, not our preferred vendor.
- Step 03
Prove
We ship one working version and check that the cost and the behaviour both hold up.
- Step 04
Scale
We move it into production, set the FinOps guardrails, and hand the keys to your team.
assess → architect → prove → scale
04Case studies
What changed after we showed up.
01
Case study
Advisory & Transformation
The onboarding pilot that would not ship.
A document-heavy KYC workflow at an enterprise bank had been in pilot for eleven months. The model performed; the economics did not. We rebuilt the workflow as an agentic pipeline, set per-case inference-cost controls, and moved it into the production environment under the existing governance regime.
OutcomeProduction in ten weeks. Cost per case fell far enough to keep it there, and the pilot became a standing mandate.
02
Case study
Technology M&A & IB
The inference cost the seller had not modeled.
On a buy-side review of an AI-enabled SaaS target, the acquirer asked us to test the pitch against the architecture. We ran the model stack, data provenance, and cost-at-scale ourselves rather than relying on the data room, and quantified the gap between the quoted and the real cost per user.
OutcomeThe number changed the terms before signing.
03
Case study
Startup Growth · RevenueRamp
From pre-revenue to a raise with signed pilots.
A technical founder had a working product and no repeatable buyer. We narrowed the ideal customer to a single first wedge, built the discovery and proof-of-value playbook, and tied pricing to the value the model actually delivered rather than to seats.
OutcomeThe founder walked into the raise with signed pilots, not a pitch. The pipeline did the talking.
They did not hand us a strategy and leave. They stayed until the thing was running, and then showed us how to run it without them.
Head of Data · Enterprise financial services client
05The people
No juniors. No pyramid. The partner you meet is the partner who delivers.
UNIOX.AI is a cooperative of senior operators, not a leverage-model consultancy. Every partner has shipped systems, run P&Ls, or closed transactions of their own. We keep the team senior and the engagements few, so the judgment in the proposal is the judgment in the room. We would rather be known for a handful of systems that run than a long list of reports that did not.
DubaiSan FranciscoLondonGurugramAhmedabadSingaporeOne standard everywhere
Kunal Singh Chauhan
Managing Partner · Gurugram
Leads AI advisory and growth. Fintech and enterprise-AI operator focused on India-native problems and proving value at POC, not just building one.
Rishi Agrawal
Partner · Transformation · Mumbai
25+ years at the intersection of business strategy and technology. Former SVP & CTO; leads digital transformation, AI, and data engagements. IIM Calcutta.
Naseer Ahmed
Partner · Capital · San Francisco
Investor and operator across Silicon Valley deep tech and venture. Leads M&A and capital mandates with hands-on technical diligence.
AS
Aditya Singh
Partner · Singapore
Leads APAC engagements, spanning enterprise AI execution and go-to-market for the region.
Operating across Dubai, Abu Dhabi, San Francisco, London, Gurugram, Ahmedabad, and Singapore. Advisory board to be announced.
06Responsible AI
We would rather catch it in review than read about it later.
Responsible AI is not a service we sell. It is the condition for everything we ship, whether we are building, growing, or doing diligence.
Independent, by design
We resell nothing and take no platform commissions. Fifty-plus platforms evaluated, and the only side we are on is yours.
Nothing ships unexamined
Bias, provenance, hallucination risk, and human oversight are settled before launch, not after the incident.
Secure and sovereign
Your data stays yours, in your region, under your regulator. Sovereign hosting is not an upsell. It is the default where the law demands it.
You keep the keys
The data, the systems, the runbooks. You could fire us tomorrow and everything keeps running. That is the point.
07Questions, answered
The things people ask before the first call.
What does UNIOX.AI do?
UNIOX.AI is an AI advisory firm. We help founders ship AI products, enterprises move AI from pilot to production, and investors run technical diligence on AI deals. Every engagement is led by senior people and ends in a working system, not a slide deck.
How are you different from a Big Four AI practice?
The partner who scopes your engagement is the practitioner who delivers it. You get senior judgment on every call, faster decisions, and no junior team learning on your budget. We take on a limited number of clients so each one gets real attention.
What is AI FinOps?
AI FinOps is the practice of managing the cost and unit economics of AI systems. It means knowing your cost per query, per user, and per model before you scale, then setting controls so inference spend stays predictable. We build it in from the start rather than after the bill arrives.
How fast can you get an AI system into production?
Most of our build engagements reach production in eight to twelve weeks, depending on data readiness and governance needs. We start with a short assessment, ship one working version, and check that the cost and the behaviour both hold up before we scale it.
Do you work with regulated industries?
Yes. We work across financial services, government, and other regulated sectors. Every engagement carries a Responsible AI checkpoint covering bias, data provenance, and human oversight, and we support sovereign or in-region hosting where your regulator requires it.
Get in touch
Tell us what you are trying to do with AI.
A senior partner reads every note and replies. No SDR, no funnel, no form you fill out three times.
hello@uniox.aiDubaiSan FranciscoLondonGurugramAhmedabadSingapore